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Mattias Heinrich

mattiaspaul

  •  Germany
  •  University of Lübeck
  •  Institute of Medical Informatics
  •  Website
Statistics
  • Member for 4 years, 10 months
  • 77 challenge submissions
  • 19 algorithms run

Activity Overview

VESSEL12 Logo
VESSEL12
Challenge User

The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.

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LOLA11
Challenge User

The goal of LOLA11 (LObe and Lung Analysis 2011) is to compare methods for (semi-)automatic segmentation of the lungs and lobes from chest computed tomography scans. Any team, whether from academia or industry, can join.

SLIVER07 Logo
SLIVER07
Challenge User

The goal of this competition is to compare different algorithms to segment the liver from clinical 3D computed tomography (CT) scans.

CHAOS Logo
CHAOS
Challenge User

In this challenge, you segment the liver in CT data, and segment liver, spleen, and kidneys in MRI data.

curious2019 Logo
curious2019
Challenge User

MICCAI Challenge 2019 for Correction of Brainshift with Intra-Operative Ultrasound. Taks 1: Register pre-operative MRI to iUS before tumor resection;Taks 2: Register iUS after tumor resection to iUS before tumor resection

EndoVisSub2019-SCARED Logo
Stereo Correspondence and Reconstruction of Endoscopic Data
Challenge User

Stereo Correspondence and Reconstruction of Endoscopic Data

VerSe2020 Logo
VerSe'20
Challenge User

Vertebrae labelling and segmentation on a multi-centre, multi-scanner, and anatomically-diverse CT dataset.

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RibFrac
Challenge User

Rib Fracture Detection and Classification Challenge: A large-scale benchmark of 660 CT scans with ~5,000 rib fractures (around 80Gb)

Learn2Reg Logo
Learn2Reg
Challenge Editor

Challenge on medical image registration addressing: learning from small datasets; estimating large deformations; dealing with multi-modal scans; and learning from noisy annotations

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Cross-Modality Domain Adaptation Image Segmentation - 2021
Challenge User

The CrossMoDA challenge 2021 introduces the first large and multi-class medical dataset for unsupervised cross-modality Domain Adaptation.

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FLARE21
Challenge User

Fast and Low GPU memory Abdominal oRgan sEgmentation Challenge

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KiPA22 (Regular Challenge)
Challenge User

The challenge is aimed to segment kidney, renal tumors, arteries, and veins from computed tomography angiography (CTA) images in one inference.

Parse2022 Logo
Parse2022
Challenge User

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.

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3D Teeth Scan Segmentation and Labeling Challenge MICCAI2022
Challenge User

Computer-aided design (CAD) tools have become increasingly popular in modern dentistry for highly accurate treatment planning. In particular, in orthodontic CAD systems, advanced intraoral scanners (IOSs) are now widely used as they provide precise digital surface models of the dentition. Such models can dramatically help dentists simulate teeth extraction, move, deletion, and rearrangement and therefore ease the prediction of treatment outcomes. Although IOSs are becoming widespread in clinical dental practice, there are only few contributions on teeth segmentation/labeling available in the literature and no publicly available database. A fundamental issue that appears with IOS data is the ability to reliably segment and identify teeth in scanned observations. Teeth segmentation and labelling is difficult as a result of the inherent similarities between teeth shapes as well as their ambiguous positions on jaws.

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ACROBAT 2023
Challenge User

The ACROBAT challenge aims to advance the development of WSI registration algorithms that can align WSIs of IHC-stained breast cancer tissue sections to corresponding tissue regions that were stained with H&E. All WSIs originate from routine diagnostic workflows.

AMOS22 Logo
Multi-Modality Abdominal Multi-Organ Segmentation Challenge 2022
Challenge User

ATM22 Logo
Multi-site, Multi-Domain Airway Tree Modeling (ATM’22)
Challenge User

Airway segmentation is a crucial step for the analysis of pulmonary diseases including asthma, bronchiectasis, and emphysema. The accurate segmentation based on X-Ray computed tomography (CT) enables the quantitative measurements of airway dimensions and wall thickness, which can reveal the abnormality of patients with chronic obstructive pulmonary disease (COPD). Besides, the extraction of patient-specific airway models from CT images is required for navigatiisted surgery.

HaN-Seg2023 Logo
The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge
Challenge User

A semantic multimodal segmentation challenge comprising 30 organs at risk

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ToothFairy: Cone-Beam Computed Tomography Segmentation Challenge
Challenge User

This is the first edition of the ToothFairy challenge organized by the University of Modena and Reggio Emilia with the collaboration of Raudboud University. This challenge aims at pushing the development of deep learning frameworks to segment the Inferior Alveolar Canal (IAC) by incrementally extending the amount of publicly available 3D-annotated Cone Beam Computed Tomography (CBCT) scans. CBCT modality is becoming increasingly important for treatment planning and diagnosis in implant dentistry and maxillofacial surgery. The three-dimensional information acquired with CBCT can be crucial to plan a vast number of surgical interventions with the aim of preserving noble anatomical structures such as the Inferior Alveolar Canal (IAC), which contains the homonymous nerve (Inferior Alveolar Nerve, IAN). Deep learning models can support medical personnel in surgical planning procedures by providing a voxel-level segmentation of the IAN automatically extracted from CBCT scans.

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SPIDER
Challenge User

UltrasoundEnhance2023 Logo
Ultrasound Image Enhancement challenge 2023
Challenge User

Pulmonary Lobe Segmentation Logo
Pulmonary Lobe Segmentation
Algorithm User

Automatic segmentation of pulmonary lobes on CT scans for patients with COPD or COVID-19.

Deep-Learning-Based CT Lung Registration Logo
Deep-Learning-Based CT Lung Registration
Algorithm User

Registration of inspiration and expiration lung CT scans using a neural network trained with multiple anatomical constraints.

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corrField
Algorithm Editor

Correspondence fields for large motion image registration

Lobe-Wise Lung Function Estimation from CT Logo
Lobe-Wise Lung Function Estimation from CT
Algorithm User

Produces patient-level and lobe-level estimates of DLCO and of FEV1 and FVC pre- and post-bronchodilator